Methods and apparatuses have been developed to allow the acquisition of data related to the motion of physical objects under the control of a human subject for the purpose of providing feedback on the performance of the subject in executing the motion in order to enhance training. Objects such as golf clubs, baseball bats and tennis rackets have been outfitted with sensors such as strain gauges, gyroscopes, magnetometers, lasers and accelerometers that provide temporal signals related to the position of the object in order to quantify certain aspects of the performance of swinging the object by a student. The feedback is typically presented as aural or tactile signals that are provided during the swing to signal either proper or improper technique.
Methods have been developed for the programming of industrial robots to execute complex tasks by recording the movements of human operators in performing the desired tasks and translating the recorded motions to a format that can be directly executed by the robot. In these cases, the objective is for the robot to autonomously perform the task essentially free of any human involvement and with higher efficiency than a human operator. Thus, these industrial robots typically operate outside the limitations of human constraints, employing forces or ranges of motion that could severely injure a human.
It is well recognized that the learning of complex motor skills in humans is enhanced by directly stimulating what is termed “muscle memory”, otherwise known as motor learning. When a movement is repeated over time, a long-term muscle memory is created for that task that eventually allows it to be performed without conscious effort. This process decreases the need for attention, enables the student to accurately repeat the motion even when under pressure to perform, and creates maximum efficiency within the motor and memory systems. Examples of muscle memory are found in everyday activities that become automatic and improve with practice, such as riding a bicycle, typing on a keyboard, playing a musical instrument, hitting a baseball or swinging a golf club. One way to accelerate motor learning is to cause the limbs of a student to repeatedly trace a desired motion as established by a competent instructor without otherwise interfering with the student's environment. Thus, for example, an industrial robot could be programmed to repeatedly swing a baseball bat or a golf club in a trajectory provided by an instructor and in such a way as to allow the student to naturally and comfortably grip the bat or club while maintaining a normal stance.
Although a competent instructor could conceivably enter swing trajectory information in parametric form into a motion control computer to provide the basis for the robot movement, it is much more natural and efficient to record the temporal parameters of the swing trajectory as performed either by the instructor or the student using a suitably instrumented appliance, then translate those parameters into robot motion control instructions while ensuring careful processing of the recorded data to avoid unintentional motion that could cause injury to the student. Thus, there is a need for a method for acquiring and processing swing trajectory data and translating it to motion control instructions for a suitably designed robot to smoothly replicate the instructor's swing for the student to experience safely.